Reducing the precision of model weights can make deep neural networks run faster in less GPU memory, while preserving model accuracy. If ever there were a salient example of a counter-intuitive ...

It turns out the rapid growth of AI has a massive downside: namely, spiraling power consumption, strained infrastructure and runaway environmental damage. It’s clear the status quo won’t cut it ...

What if the future of artificial intelligence wasn’t about building bigger, more complex models, but instead about making them smaller, faster, and more accessible? The buzz around so-called “1-bit ...

Huawei’s Computing Systems Lab in Zurich has introduced a new open-source quantization method for large language models (LLMs) aimed at reducing memory demands without sacrificing output quality.

One-bit large language models (LLMs) have emerged as a promising approach to making generative AI more accessible and affordable. By representing model weights with a very limited number of bits, ...

Right now, everyone is seeing a boom in the ways that people are innovating large language models. Whether you believe that people are engineering these systems or merely discovering them, knowing ...

Large language models (LLMs) have become crucial tools in the pursuit of artificial general intelligence (AGI). However, as the user base expands and the frequency of usage increases, deploying these ...

Why does it sometimes feel like the tools we rely on are getting worse, not better? Imagine asking a innovative AI model a question, only to receive a response that feels oddly incoherent or ...

Recently AI risk and benefit evaluation company METR ran a randomized control test (RCT) on a gaggle of experienced open source developers to gain objective data on how the use of LLMs affects their ...

There have been a number of high-profile cases where scientific papers have had to be retracted because they were filled with AI-generated slop—the most recent coming just two weeks ago. These ...